AI, Machine Learning and Deep Learning: A Security Perspective

Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Table of Contents:
Part I. Secure AI/ML Systems: Attack Models 1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications, 3. Threat of Adversarial Attacks to Deep Learning: A Survey, 4. Attack Models for Collaborative Deep Learning, 5. Attacks on Deep Reinforcement Learning Systems: A Tutorial, 6. Trust and Security of Deep Reinforcement Learning, 7. IoT Threat Modeling using Bayesian Networks Part II. Secure AI/ML Systems: Defenses 8. Survey of Machine Learning Defense Strategies, 9. Defenses Against Deep Learning Attacks, 10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning, 11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems, 12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data Regulation Part III. Using AI/ML Algorithms for Cyber Security 13. Using Machine Learning for Cyber Security: Overview, 14. Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security, 15. Using ML and DL Algorithms for Intrusion Detection in Industrial Internet of Things. Part IV. Applications 16. On Detecting Interest Flooding Attacks in Named Data Networking (NDN)-based IoT Searches, 17. Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks, 18. An Artificial Intelligence-assisted Security Analysis of Smart Healthcare Systems, 19. A User-centric Focus for Detecting Phishing Emails